@article {4786,
	title = {Memory as a Computational Resource},
	journal = {Trends in Cognitive Sciences},
	volume = {25},
	year = {2021},
	month = {03/2021},
	pages = {240 - 251},
	abstract = {<p>Most computations that people do in everyday life are very expensive. Recent research highlights that humans make efficient use of their limited computational resources to tackle these problems. Memory is a crucial aspect of algorithmic efficiency and permits the reuse of past computation through memoization. We review neural and behavioral evidence of humans reusing past computations across several domains, including mental imagery, arithmetic, planning, and probabilistic inference. Recent developments in neural networks expand the scope of computational reuse with a distributed form of memoization called amortization. This opens many new avenues of research. Computer scientists have long recognized that naive implementations of algorithms often result in a paralyzing degree of redundant computation. More sophisticated implementations harness the power of memory by storing computational results and reusing them later. We review the application of these ideas to cognitive science, in four case studies (mental arithmetic, mental imagery, planning, and probabilistic inference). Despite their superficial differences, these cognitive processes share a common reliance on memory that enables efficient computation.</p>
},
	keywords = {amortization, inference, memory, mental arithmetic, mental imagery, planning},
	issn = {13646613},
	doi = {10.1016/j.tics.2020.12.008},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1364661320303053},
	author = {Ishita Dasgupta and Samuel J Gershman}
}
